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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 577 records · Page 32
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Beyond BioSentinel: Iterative Development of Automated Microfluidics

NASA Ames has flown a series of Bio-CubeSats that performed biology experiments supported by automated fluidic systems. Since Genesat-1 in 2006, these payloads have increased in complexity and functionality, building upon previous successes, and applying lessons learned. BioSentinel was the most recent of this series, launched into heliocentric orbit onboard Artemis-1 in 2022. This presentation will discuss how the fluidic technology developed for these Bio-CubeSat missions, including the multi-layer polycarbonate manifolds at the heart of the BioSentinel BioSensor, have spurred the development of several additional projects. Most directly is the modified BioSensor that will be a part of LEIA, which will perform its Lunar biology experiment onboard a Commercial Lunar Payload Services lander. Several search-for-life manifolds have been designed to prepare samples from icy moons for downstream analyses. Two early career Polaris projects are developing fluidics to perform genetic sequencing on samples from multigenerational cell culture and to extract and quantify target miRNAs to support astronaut radiation health assessment. Improving the readiness of these systems has been accelerated by adopting the established flight heritage and microgravity-compatibility of the Bio-CubeSat fluidic hardware and designs, while focusing development efforts on the integration of novel functionalities and components.

microfluidics↗
DOE OSTI2021

Full-field imaging learning machine (FILM)

A method of determining dynamic properties of a structure (linear or nonlinear) includes receiving spatio-temporal inputs, generating mode shapes and modal components corresponding to the spatio-temporal inputs using a trained deep complexity coding artificial neural network, and subsequently generating the dynamic properties by analyzing each modal component using a trained learning machine. A computing system for non-contact determination of dynamic properties of a structure includes a camera, a processor, and a memory including computer-executable instructions. When the instructions are executed, the system is caused to receive spatio-temporal image data, decompose the spatio-temporal image data into constituent manifold components using an autoencoder, and analyze the constituent manifold components using a trained learning machine to determine the dynamic properties.

Yang, Yongchao↗
DOE OSTI2021

Portable unit for metabolic analysis

A portable unit for metabolic analysis includes a manifold in fluid communication with a mouth of a test subject. A conduit extends from the manifold and defines a flow channel through which the breaths of the test subject travels. A carbon dioxide sensor is attached to the conduit. The carbon dioxide sensor includes an intermediate support housing enclosing a volume disposed along the flow channel to define a first part of the flow channel, an inner support housing attached to a first side of the intermediate support housing, a plurality of IR LEDs mounted to an interior surface of the inner support housing, an outer support housing attached to a second side of the intermediate support housing, and a photodetector attached to the outer support housing. The photodetector faces the flow channel and receives IR radiation emitted by the plurality of LEDs.

Dietrich, Daniel L.↗
DOE OSTI2022

Inverted heat exchanger device

An inverted heat exchanger device includes an exterior conduit elongated and extending around a center axis between a first end and second end. The exterior conduit including a body having an exterior surface, an interior surface, a center core elongated along the center axis, and plural walls extending between the center core and the interior surface. A first conduit is disposed inside the exterior conduit that includes an inlet, plural core passages, an outlet, and internal manifolds. A first fluid is configured to flow along the first conduit. A second conduit is also disposed inside the exterior conduit. The second conduit includes an inlet, plural core passages, an outlet, and internal manifolds. A second fluid is configured to flow along the second conduit. The plural walls are configured to define the first conduit and the second conduit within the body of the exterior conduit.

de Bock, Hendrik Pieter Jacobus↗
DOE OSTI2023

Regenerative solid oxide stack

An individual solid oxide cell (SOC) constructed of a sandwich configuration including in the following order: an oxygen electrode, a solid oxide electrolyte, a fuel electrode, a fuel manifold, and at least one layer of mesh. In one embodiment, the mesh supports a reforming catalyst resulting in a solid oxide fuel cell (SOFC) having a reformer embedded therein. The reformer-modified SOFC functions internally to steam reform or partially oxidize a gaseous hydrocarbon, e.g. methane, to a gaseous reformate of hydrogen and carbon monoxide, which is converted in the SOC to water, carbon dioxide, or a mixture thereof, and an electrical current. In another embodiment, an electrical insulator is disposed between the fuel manifold and the mesh resulting in a solid oxide electrolysis cell (SOEC), which functions to electrolyze water and/or carbon dioxide.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Heat exchangers and related systems and methods

A heat exchanger may include a main body with an inlet plenum and an outlet plenum at a first end, and a header at a second end. At least one elongated shaft may extend from the outlet plenum to the header. At least one heat pipe may be coupled to the header and a portion of each heat pipe may be positioned within a corresponding elongated shaft defining an annular space between each heat pipe and each corresponding elongated shaft. A flow skirt may include a manifold located between the inlet plenum and the outlet plenum of the main body. At least one elongated tube may extend from the manifold. Each elongated tube may be positioned within a corresponding annular space between each heat pipe and each corresponding elongated shaft, dividing the annular space into two concentric annular channels comprising an inner annular channel and an outer annular channel.

Yoon, SuJong↗
DOE OSTI2023

Hollow lattice thermal energy storage heat exchanger

Example heat exchangers and methods of use are described herein. An example heat exchanger includes a lattice structure including a plurality of conduits defining a plurality of interstitial voids between the plurality of conduits. Each of the plurality of conduits includes an inlet and an outlet, and the plurality of conduits are arranged such that, between the inlet and the outlet, each of the conduits intersects at least one other conduit to enable flow between the intersecting conduits. The example heat exchanger also includes a first manifold formed unitarily with the lattice structure, the first manifold comprising a first plurality of openings in fluid communication with each inlet of the plurality of conduits. The example heat exchanger further includes a phase change material (PCM) disposed within and substantially filling the plurality of interstitial voids.

Stoia, Michael F.↗
DOE OSTI2023

Main mixer in an axial staged combustor for a gas turbine engine

A main mixer including a swirler along an axis, the swirler including an outer swirler with a multiple of outer vanes, and a center swirler with a multiple of center vanes and a swirler hub along the axis, the swirler hub including a fuel manifold and an inner swirler with a multiple of inner vanes that support a centerbody, the multiple of inner vanes interconnect the fuel manifold and the centerbody.

Dai, Zhongtao↗
DOE OSTI2024

Chiral symmetry and Atiyah-Patodi-Singer index theorem for staggered fermions

We consider the Atiyah-Patodi-Singer (APS) index theorem corresponding to the chiral symmetry of a continuum formulation of staggered fermions called Kähler-Dirac fermions, which have been recently investigated as an ingredient in lattice constructions of chiral gauge theories. We point out that there are two notions of chiral symmetry for Kähler-Dirac fermions, both having a mixed perturbative anomaly with gravity leading to index theorems on closed manifolds. By formulating these theories on a manifold with boundary, we find the APS index theorems corresponding to each of these symmetries, necessary for a complete picture of anomaly inflow, using a recently discovered physics-motivated proof. We comment on a fundamental difference between the nature of these two symmetries by showing that a sensible local, symmetric boundary condition only exists for one of the two symmetries. This sheds light on how these symmetries behave under lattice discretization, and in particular on their use for recent symmetric-mass generation proposals.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Using "AI Poincare" to analyze non-linear integrable optics

This study dives into the applicability of using automated discovery of conserved quantities in dynamical systems relevant to accelerator physics. Specifically, we explore the performance of AI Poincaré in analyzing numerical trajectory data obtained using the McMillan system of non-linear integrable optics. A comprehensive evaluation of the algorithm's performance is conducted through diverse methodologies. These include the analysis of the estimated number of conserved quantities embedded in a dataset and the deviation of interpolated points on the inferred manifold with respect to points in actually in the dataset. the investigation identifies an optimal range of perturbation distances where the underlying manifold extraction algorithm inside AI Poincaré exhibits optimal performance. Additionally, an improved neural network architecture is proposed based on the observed results. Finally, we apply the algorithm to preliminary experimental data from the Integrable Optics Test Accelerator at Fermilab to successfully infer the number of conserved quantities even in the presence of fast decoherence of the measured signal.

Osmanov, Lazare [Free U. Tbilisi]↗
DOE OSTI2026

Strong-Drive Limits in Josephson Circuits: From Chaos to an Unbound-Resonance Threshold

Strong microwave drives enable fast measurement and parametric control in superconducting circuits but can induce transitions out of the intended low-energy manifold. We develop a unified description of strong-drive limits in flux- and charge-driven Josephson circuits across drive frequency and dc flux bias. Using classical phase-space analysis and Floquet--Markov simulations, we identify distinct low- and high-frequency mechanisms. At low frequency, we characterize bound-state resonances and separatrix chaos and find that the flux-drive chaos threshold depends strongly on dc flux bias. At high frequency, these mechanisms are suppressed, and the dissipative steady state transfers from the central bound-state sector to outer resonances formed by above-barrier running trajectories. The resulting unbound-resonance threshold is nearly independent of drive frequency and circuit parameters over the regime studied and is controlled primarily by dc flux bias. Coherent simulations show that parametric operation persists beyond this threshold, but at a reduced rate, setting an effective upper bound on the achievable operation speed. We derive analytical criteria for both thresholds, validate them numerically, and experimentally confirm the predicted dc-bias dependence of the low-frequency threshold in a flux-driven SQUID. We also determine the timescales for transfer into the unbound-resonance regime and relaxation back to the bound-state manifold after the drive is removed. Finally, we relate the stability limits to a complementary picture based on the junction critical current and extend the framework to multitone drives and inductively shunted circuits. Together, these results identify the mechanisms limiting strong driving and suggest routes to extend the stable operating range of Josephson circuits.

You, Xinyuan [Fermilab] (ORCID:0000000291789419)↗
DOE OSTI2020

Design complexity and performance analysis in additively manufactured heat exchangers

The main objective of this research is to investigate the tradeoffs between design complexity and thermal performance in additively manufactured metallic heat exchangers. Such heat exchangers have become a favorite topic for investigation in various fields ranging from air conditioning to aircraft gas turbine engines. In particular, high manufacturing cost has emerged as the major drawback in broad applications of manifold-microchannel heat exchanger (MMHXs). The possibility of cost-effective manufacturing has generated wide interest in applying additive manufacturing (AM) for fabricating MMHXs. Furthermore, AM technologies will provide an opportunity for enhanced design and superior heat transfer performance. In this study, innovative MMHXs are designed and fabricated through selective laser melting. An experimental setup was designed to measure pressure drop and heat transfer for the developed MMHXs with a wide range of Reynolds numbers. Furthermore, the coefficient of performance was calculated based on the heat flow rate, air pressure drop, and air flow rate. Measurement results demonstrated that in some scenarios adding more internal features, i.e., increasing design complexity, does not necessarily improve the MMHX performance.

10 SYNTHETIC FUELS↗
DOE OSTI2024

Finite elements for Matérn-type random fields: Uncertainty in computational mechanics and design optimization

This work highlights an approach for incorporating realistic uncertainties into scientific computing workflows based on finite elements, focusing on prevalent applications in computational mechanics and design optimization. We leverage Matérn-type Gaussian random fields (GRFs) generated using the SPDE method to model aleatoric uncertainties, including environmental influences, variating material properties, and geometric ambiguities. Our focus lies on delivering practical GRF realizations that accurately capture imperfections and variations and understanding how they impact the predictions of computational models as well as the shape and topology of optimized designs. Here we describe a numerical algorithm based on solving a generalized SPDE to sample GRFs on arbitrary meshed domains. The algorithm leverages established techniques and integrates seamlessly with the open-source finite element library MFEM and associated scientific computing workflows, like those found in industrial and national laboratory settings. Our solver scales efficiently for large-scale problems and supports various domain types, including surfaces and embedded manifolds. We showcase its versatility through biomechanics and topology optimization applications, emphasizing the potential to influence these domains. The flexibility and efficiency of SPDE-based GRF generation empowers us to run large-scale optimization problems on 2D and 3D domains, including finding optimized designs on embedded surfaces, and to generate design features and topologies beyond the reach of conventional techniques. Moreover, these capabilities allow us to model and quantify geometric uncertainties on reconstructed submanifolds, such as the interpolated surfaces of cerebral aneurysms provided by postprocessing CT scans. In addition to offering benefits in these specific domains, the proposed techniques transcend specific applications and generalize to arbitrary forward and backward problems in uncertainty quantification involving finite elements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Stochastic symplectic reduced-order modeling for model-form uncertainty quantification in molecular dynamics simulations in various statistical ensembles

Here, this work focuses on the representation of model-form uncertainties in molecular dynamics simulations in various statistical ensembles. In prior contributions, the modeling of such uncertainties was formalized and applied to quantify the impact of, and the error generated by, pair-potential selection in the microcanonical ensemble (NVE). In this work, we extend this formulation and present a linear-subspace reduced-order model for the canonical (NVT) and isobaric (NPT) ensembles. The symplectic reduced-order basis is randomized on the tangent space of the Stiefel manifold to provide topological relationships and capture model-form uncertainty. Using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), we assess the relevance of these stochastic reduced-order atomistic models on canonical problems involving a Lennard-Jones fluid and an argon crystal melt.

42 ENGINEERING↗
DOE OSTI2026

Interpretable and flexible non-intrusive reduced-order models using reproducing kernel Hilbert spaces

This paper develops an interpretable, non-intrusive reduced-order modeling technique using regularized kernel interpolation. Existing non-intrusive approaches approximate the dynamics of a reduced-order model (ROM) by solving a data-driven least-squares regression problem for low-dimensional matrix operators. Our approach instead leverages regularized kernel interpolation, which yields an optimal approximation of the ROM dynamics from a user-defined reproducing kernel Hilbert space. We show that our kernel-based approach can produce interpretable ROMs whose structure mirrors full-order model structure by embedding judiciously chosen feature maps into the kernel. The approach is flexible and allows a combination of informed structure through feature maps and closure terms via more general nonlinear terms in the kernel. We also derive a computable a posteriori error bound that combines standard error estimates for intrusive projection-based ROMs and kernel interpolants. In conclusion, the approach is demonstrated in several numerical experiments that include comparisons to operator inference using both proper orthogonal decomposition and quadratic manifold dimension reduction.

Data-driven model reduction↗
DOE OSTI2021

Two-color pyrometry based flame to fuel surface radiative heat flux diagnostic using flamelets

In this study, a non-intrusive two-color pyrometry based radiative heat flux diagnostic is presented that accounts for the self-absorption effects. This diagnostic relies on looking up the flame temperature and soot volume fraction using the ratio of color intensities obtained from digital cameras and numerical solutions of 1D steady-state diffusion flames. Virtual two color pyrometry of computed flame show a unique one-to-one mapping of strained flames with intensity ratio; thereby allowing flames to be indexed and creation of a two-color flamelet manifold (TCFM). The TCFM is a function of two variables; the ratio of red to green intensities (flame index) and physical distance to the stoichiometric surface defined using a level-set function. The developed diagnostic is used in upward flame spread experiments where an approximate flame hull reconstruction method is applied to provide the 3D level-set function and intensity ratio for use of TCFM to provide radiative absorption properties for 3D ray tracing. Accounting for self- absorption effects provide improved soot volume fraction estimates. In conclusion, the radiative heat flux estimates are found to be in very good agreement with literature and improved results are obtained near the flame base.

42 ENGINEERING↗
DOE OSTI2021

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physics-informed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers' equations. A speedup of up to 2.6 for 1D Burgers' and a speedup of 11.7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Lastly, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A long short-term memory embedding for hybrid uplifted reduced order models

In this paper, we introduce an uplifted reduced order modeling (UROM) approach through the integration of standard projection based methods with long short-term memory (LSTM) embedding. Our approach has three modeling layers or components. In the first layer, we utilize an intrusive projection approach to model dynamics represented by the largest modes. The second layer consists of an LSTM model to account for residuals beyond this truncation. This closure layer refers to the process of including the residual effect of the discarded modes into the dynamics of the largest scales. However, the feasibility of generating a low rank approximation tails off for higher Kolmogorov n -width systems due to the underlying nonlinear processes. The third uplifting layer, called super-resolution, addresses this limited representation issue by expanding the span into a larger number of modes utilizing the versatility of LSTM. Therefore, our model integrates a physics-based projection model with a memory embedded LSTM closure and an LSTM based super-resolution model. In several applications, we exploit the use of Grassmann manifold to construct UROM for unseen conditions. We performed numerical experiments by using the Burgers and Navier-Stokes equations with quadratic nonlinearity. Finally, our results show robustness of the proposed approach in building reduced order models for parameterized systems and confirm the improved trade-off between accuracy and efficiency.

42 ENGINEERING↗